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Introduction In the preintensive treatment era, relative mortality in type 1 diabetes (T1D) exceeded that in the population without diabetes (1,2). Although substantial declines in mortality rates have been reported with improvements in glycemic control and better treatment of cardiovascular disease (CVD) risk factors (3–11), recent reports from Scotland (12) and Sweden (13) describe a greater excess mortality in T1D, even among those with a mean HbA1c <7% (13). Recently, the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study demonstrated that intensive diabetes therapy in T1D during the DCCT yielded a 33% reduction in the risk of mortality, versus conventional diabetes therapy, over a 27-year period of follow-up (14). Herein we compare mortality during the DCCT/EDIC in the entire cohort to that in the general U.S. population using current (2013) U.S. age-, sex-, and race-specific mortality rates and assess relative mortality as a function of the level of HbA1c and sex.

ional diabetes therapy, over a 27-year period of follow-up (14). Herein we compare mortality during the DCCT/EDIC in the entire cohort to that in the general U.S. population using current (2013) U.S. age-, sex-, and race-specific mortality rates and assess relative mortality as a function of the level of HbA1c and sex. Research Design and Methods During 1983–1989, the DCCT enrolled 1,441 patients with T1D between the ages of 13 and 39 years who were randomly assigned to receive either intensive or conventional therapy. The primary objective of the DCCT was to assess the effects of intensive versus conventional therapy on the onset of retinopathy in a primary prevention cohort who entered with no retinopathy, and on the progression of retinopathy in a secondary intervention cohort who entered with preexisting mild to moderate nonproliferative retinopathy, each cohort comprising ∼700 subjects. The primary prevention cohort also had 1–5 years diabetes duration and <40 mg albuminuria per 24 h. The secondary intervention cohort had 1–15 years duration and <200 mg albuminuria per 24 h. In both cohorts, the mean age was 27 years with ∼53% male. At baseline, those with a history of CVD, hypertension, or hypercholesterolemia were excluded (15).

Research Design and Methods During 1983–1989, the DCCT enrolled 1,441 patients with T1D between the ages of 13 and 39 years who were randomly assigned to receive either intensive or conventional therapy. The primary objective of the DCCT was to assess the effects of intensive versus conventional therapy on the onset of retinopathy in a primary prevention cohort who entered with no retinopathy, and on the progression of retinopathy in a secondary intervention cohort who entered with preexisting mild to moderate nonproliferative retinopathy, each cohort comprising ∼700 subjects. The primary prevention cohort also had 1–5 years diabetes duration and <40 mg albuminuria per 24 h. The secondary intervention cohort had 1–15 years duration and <200 mg albuminuria per 24 h. In both cohorts, the mean age was 27 years with ∼53% male. At baseline, those with a history of CVD, hypertension, or hypercholesterolemia were excluded (15). The DCCT intensive therapy group was treated with insulin pumps or at least three daily insulin injections for an average of 6.5 years during which they maintained a mean HbA1c of ∼7%. Conversely, the DCCT conventional therapy group received then-standard care with a mean of HbA1c of ∼9% over the 6.5 years (15). The DCCT ended in 1993, at which time all patients were referred to their private health care providers with the recommendation that they follow an intensive regimen (16). Thereafter, 1,394 participants (representing 97% of the entire cohort) joined the EDIC observational study (1994 to present), with ongoing diabetes care provided by their local providers (16). Over the 21 years of follow-up in EDIC, the cohort maintained a mean HbA1c of ∼8%, with little difference between the DCCT intensive versus conventional therapy groups (17). The DCCT and EDIC protocols were approved by institutional review boards at all participating centers.

are provided by their local providers (16). Over the 21 years of follow-up in EDIC, the cohort maintained a mean HbA1c of ∼8%, with little difference between the DCCT intensive versus conventional therapy groups (17). The DCCT and EDIC protocols were approved by institutional review boards at all participating centers. HbA1c was measured quarterly during DCCT and annually in EDIC. The time-weighted mean HbA1c represented the total glycemic exposure during DCCT/EDIC with weights of 0.25 and 1 for quarterly DCCT and annual EDIC values, respectively, up to the time immediately preceding the event or censoring for those without an event. The updated mean HbA1c was then used as a time-dependent covariate in the regression model. Analyses herein are based on 125 deaths that occurred up to 31 October 2014. Deaths, with documentation if available, were reported to the Data Coordinating Center and were adjudicated by a within-study Mortality and Morbidity Review Committee (14). There were 1,316 survivors, 1,241 of whom were under active follow-up whose observation time was right censored at 31 October 2014 and 75 of whom were inactive whose observation time was right censored at the date last known to be alive. Details of the ascertainment of outcomes and the verification of vital status were recently described (14).

s, 1,241 of whom were under active follow-up whose observation time was right censored at 31 October 2014 and 75 of whom were inactive whose observation time was right censored at the date last known to be alive. Details of the ascertainment of outcomes and the verification of vital status were recently described (14). The 2013 population life tables from the National Center for Health Statistics presented sex- and race-specific mortality risks in the general population for each year of age (18). The expected number of deaths in the DCCT cohort assuming these general population risks was calculated using the indirect method (19). For each subject of a given sex and race, the population probability of death over each year of age during DCCT/EDIC follow-up was applied. The sum of these probabilities for all subjects is the number of deaths in the DCCT/EDIC cohort that were expected had the current age-, sex-, and race-specific population risks been applied. The standardized mortality ratio (SMR) was computed as the ratio of the observed to expected number of deaths. All SMRs presented herein were computed in this manner.

subjects is the number of deaths in the DCCT/EDIC cohort that were expected had the current age-, sex-, and race-specific population risks been applied. The standardized mortality ratio (SMR) was computed as the ratio of the observed to expected number of deaths. All SMRs presented herein were computed in this manner. Death rates per 100,000 person-years (PY) and 95% CIs were computed from robust Poisson regression models (20). Additional robust Poisson models using the PY method (21) assessed the effect of covariates, including the time-dependent updated mean HbA1c, on the relative mortality rate (RMR) for DCCT/EDIC versus the general population, with offset terms that account for the expected mortality based on age, sex, and race. The RMR can be viewed as a covariate-adjusted estimate of the ratio of SMRs for two groups, or as the increase in the SMR per unit increase in a quantitative covariate. Semiparametric mortality risk gradients with respect to the time-dependent mean HbA1c values are presented using plots from Poisson additive models with smoothing splines (df = 4) (22). Similar analyses were used to investigate whether the age- and sex-specific mortality rates in this cohort of participants with T1D differed from the general population. All analyses were performed using SAS software (version 9.3; SAS Institute, Cary, NC) and the R package. Two-sided P ≤ 0.05 was considered statistically significant.

Death rates per 100,000 person-years (PY) and 95% CIs were computed from robust Poisson regression models (20). Additional robust Poisson models using the PY method (21) assessed the effect of covariates, including the time-dependent updated mean HbA1c, on the relative mortality rate (RMR) for DCCT/EDIC versus the general population, with offset terms that account for the expected mortality based on age, sex, and race. The RMR can be viewed as a covariate-adjusted estimate of the ratio of SMRs for two groups, or as the increase in the SMR per unit increase in a quantitative covariate. Semiparametric mortality risk gradients with respect to the time-dependent mean HbA1c values are presented using plots from Poisson additive models with smoothing splines (df = 4) (22). Similar analyses were used to investigate whether the age- and sex-specific mortality rates in this cohort of participants with T1D differed from the general population. All analyses were performed using SAS software (version 9.3; SAS Institute, Cary, NC) and the R package. Two-sided P ≤ 0.05 was considered statistically significant. Results Characteristics of the DCCT/EDIC cohort used for these analyses were recently described (14). In brief, on entry, subjects had a mean age of 27 years (now 55 years) with 6 years duration of diabetes (now 34 years) and 48% were female. Those who subsequently died were older, had an older age at diabetes onset, and were more likely to be male, be smokers, and to have higher baseline blood pressure, triglycerides, and HbA1c levels (13). Among 125 observed deaths, the primary underlying causes were CVD (n = 29, 23.2%) and cancer (n = 25, 20%), followed by T1D (n = 14, 11.2%), accident (n = 11, 8.8%), suicide (n = 8, 6.4%), renal disease (n = 7, 5.6%), and other (25, 20%), plus 2 pending adjudication and 4 nonadjudicable.

cerides, and HbA1c levels (13). Among 125 observed deaths, the primary underlying causes were CVD (n = 29, 23.2%) and cancer (n = 25, 20%), followed by T1D (n = 14, 11.2%), accident (n = 11, 8.8%), suicide (n = 8, 6.4%), renal disease (n = 7, 5.6%), and other (25, 20%), plus 2 pending adjudication and 4 nonadjudicable. SMRs Table 1 presents the SMRs comparing the mortality experience in the DCCT/EDIC cohort by treatment group, cohort, and sex, individually and jointly. The observed number of deaths, and the number expected when the population risks are applied to the cohort, the observed rate per 100,000 PY, and the SMR with its 95% CI are shown. During a total of 39,082 patient-years of follow-up in the DCCT/EDIC cohort, all-cause mortality was 320/100,000 PY (95% CI 269, 380). This overall mortality did not exceed that expected in the current U.S. population (SMR = 1.09 [95% CI 0.92, 1.30]) (Table 1). Table 1 DCCT/EDIC deaths and death rates by DCCT intensive versus conventional therapy group, primary versus secondary cohort, and sex, with SMRs relative to the U.S. population, along with RMRs comparing two SMRs

SMRs Table 1 presents the SMRs comparing the mortality experience in the DCCT/EDIC cohort by treatment group, cohort, and sex, individually and jointly. The observed number of deaths, and the number expected when the population risks are applied to the cohort, the observed rate per 100,000 PY, and the SMR with its 95% CI are shown. During a total of 39,082 patient-years of follow-up in the DCCT/EDIC cohort, all-cause mortality was 320/100,000 PY (95% CI 269, 380). This overall mortality did not exceed that expected in the current U.S. population (SMR = 1.09 [95% CI 0.92, 1.30]) (Table 1). Table 1 DCCT/EDIC deaths and death rates by DCCT intensive versus conventional therapy group, primary versus secondary cohort, and sex, with SMRs relative to the U.S. population, along with RMRs comparing two SMRs Observed/expected* Rate (95% CI)† SMR (95% CI)‡ RMR (95% CI)§ P Total (n = 1,441) 125/114 320 (269, 380) 1.09 (0.92, 1.30) Intensive (n = 711) 51/58 263 (200, 345) 0.88 (0.67, 1.16) 1.49 (1.04, 2.14) 0.028 Conventional (n = 730) 74/56 376 (301, 470) 1.31 (1.05, 1.65) Primary (n = 726) 61/54 315 (247, 404) 1.13 (0.88, 1.45) 0.95 (0.67, 1.35) 0.76 Secondary (n = 715) 64/60 324 (255, 412) 1.07 (0.83, 1.36) Females (n = 680) 47/39 252 (190, 333) 1.19 (0.90, 1.59) 0.87 (0.61, 1.26) 0.464 Males (n = 761) 78/75 382 (307, 475) 1.04 (0.83, 1.30) Treatment group by sex Conventional vs. Intensive Females Intensive 21/21 220 (145, 335) 0.99 (0.64, 1.51) 1.46 (0.82, 2.59) 0.201 Conventional 26/18 284 (195, 415) 1.44 (0.98, 2.11) Males Intensive 30/37 304 (213, 434) 0.82 (0.57, 1.18) 1.54 (0.97, 2.43) 0.066 Conventional 48/38 456 (346, 600) 1.26 (0.95, 1.66) Treatment group by study cohort Conventional vs. Intensive Primary Intensive 27/26 291 (200,422) 1.03 (0.70, 1.51) 1.17 (0.71, 1.95) 0.538 Conventional 34/28 338 (244,470) 1.21 (0.87, 1.69) Secondary Intensive 24/32 237 (160,353) 0.75 (0.50, 1.13) 1.88 (1.13, 3.12) 0.015 Conventional 40/28 415 (307,562) 1.42 (1.04, 1.93) *Number of deaths.

udy cohort Conventional vs. Intensive Primary Intensive 27/26 291 (200,422) 1.03 (0.70, 1.51) 1.17 (0.71, 1.95) 0.538 Conventional 34/28 338 (244,470) 1.21 (0.87, 1.69) Secondary Intensive 24/32 237 (160,353) 0.75 (0.50, 1.13) 1.88 (1.13, 3.12) 0.015 Conventional 40/28 415 (307,562) 1.42 (1.04, 1.93) *Number of deaths. †Rate per 100,000 PY with 95% CI from a Poisson regression model with robust information sandwich standard errors. ‡Expected number of deaths from the 2013 U.S. population life table for every year of age in the cohort and the SMR. §RMR obtained from an unadjusted Poisson model, each with 95% CI and P value (two sided). Table 1 also shows that the mortality rate was lower in the DCCT intensive than conventional therapy group (263 vs. 376 per 100,000 PY). The SMR in the DCCT conventional therapy group was 49% higher than that in the intensive therapy group (RMR = 1.49, P = 0.028). Mortality in the DCCT intensive therapy group was lower than that in the general U.S. population, although not significantly so (SMR = 0.88 [95% CI 0.67, 1.16]), whereas mortality in the DCCT conventional therapy group was significantly greater than that in the general population (SMR = 1.31 [95% CI 1.05, 1.65], P = 0.018).

lity in the DCCT intensive therapy group was lower than that in the general U.S. population, although not significantly so (SMR = 0.88 [95% CI 0.67, 1.16]), whereas mortality in the DCCT conventional therapy group was significantly greater than that in the general population (SMR = 1.31 [95% CI 1.05, 1.65], P = 0.018). The RMR comparing the SMR of the secondary versus primary cohorts (1.07 vs. 1.13) was not significant (RMR = 0.95). Even though DCCT/EDIC males had a higher risk of mortality than females in a Cox proportional hazards model (HR = 1.61, P = 0.02) (see Orchard et al. [14]), the SMR for males was slightly less than that for females (1.04 vs. 1.19) and the RMR for males versus females was not significant (RMR = 0.87). Among females alone, the SMRs in the DCCT conventional and intensive therapy groups (1.44 and 0.99, respectively) were similar to those in the overall cohort, as was the RMR (RMR = 1.46, P = 0.201). Among males, likewise, the SMRs in the two groups (1.26 and 0.82) were similar to those in the overall cohort, as was the RMR (RMR = 1.54, P = 0.066) (Table 1). Within the primary cohort, the RMR comparing the SMRs in the DCCT conventional versus intensive therapy groups (1.21 vs. 1.03) was not significant (RMR = 1.17). Within the secondary cohort, the DCCT conventional therapy group SMR was nominally significant (SMR = 1.42 [95% CI 1.04, 1.93], P = 0.027) and was significantly higher than that in the DCCT intensive therapy group (SMR = 0.75), with an RMR = 1.88 (P = 0.015).

apy groups (1.21 vs. 1.03) was not significant (RMR = 1.17). Within the secondary cohort, the DCCT conventional therapy group SMR was nominally significant (SMR = 1.42 [95% CI 1.04, 1.93], P = 0.027) and was significantly higher than that in the DCCT intensive therapy group (SMR = 0.75), with an RMR = 1.88 (P = 0.015). Role of HbA1c and Sex Glycemic exposure measured as the updated mean HbA1c (time dependent) was significantly associated with mortality (P < 0.0001), with each 1% increase in the mean HbA1c corresponding to a 74% increase (95% CI 53, 98) in the mortality rate relative to the age-, sex-, and race-specific rates in the general population. Figure 1 further describes this relationship by providing the mortality rates relative to the U.S. population over a range of HbA1c values. The model assumes that the log of the RMR is a linear function of the HbA1c that was largely verified by examining a spline-smoothed estimate of the relationship. Figure 1 The RMR for the mortality in the combined DCCT/EDIC cohort relative to the age-, sex-, and race-specific risk in the general population as a function of the updated time-dependent mean HbA1c during the DCCT and EDIC from a Poisson regression model. The horizontal dashed line at an RMR of 1.0 represents no difference in risk relative to the general population.

DCCT/EDIC cohort relative to the age-, sex-, and race-specific risk in the general population as a function of the updated time-dependent mean HbA1c during the DCCT and EDIC from a Poisson regression model. The horizontal dashed line at an RMR of 1.0 represents no difference in risk relative to the general population. Figure 1 shows a largely flat relationship with a RMR <1 for periods of time with HbA1c values ≤8% but an exponential rise in the SMR for periods with HbA1c values >9%. Although only 7.8% of the mean HbA1c values were >10% over the entire study period, 31 deaths (24.8%) occurred in subjects whose updated mean HbA1c value was then >10%. In additional models adjusting for the time-dependent mean HbA1c values, there was a significant interaction between sex and HbA1c (P = 0.016), such that as the HbA1c increased, the relative mortality among females was increasingly greater than that among males. RMRs compared with the age-, sex-, and race-specific rates are presented in Fig. 2 separately by sex over a range of HbA1c values. For both males and females, the RMR is ≤1 for periods where the mean HbA1c is <9%, but the relative rate increases exponentially for values of HbA1c >9%, significantly more so among females. Age was not associated with the relative mortality of this cohort (P = 0.42), i.e., as mortality increased with increasing age, the SMR did not.

and females, the RMR is ≤1 for periods where the mean HbA1c is <9%, but the relative rate increases exponentially for values of HbA1c >9%, significantly more so among females. Age was not associated with the relative mortality of this cohort (P = 0.42), i.e., as mortality increased with increasing age, the SMR did not. Figure 2 The RMR for the mortality in the combined DCCT/EDIC cohort relative to the age-, sex-, and race-specific risk in the general population as a function of the updated time-dependent mean HbA1c during the DCCT and EDIC separately for males and females. Conclusions Relative to the age-, sex-, and race-specific mortality rates for the current general U.S. population, overall mortality in the DCCT/EDIC cohort was not significantly increased (SMR = 1.09 [95% CI 0.92, 1.3]). However, the relative mortality in the DCCT intensive therapy group (SMR = 0.88) was nonsignificantly lower (i.e., neutral), whereas that in the DCCT conventional therapy group was significantly higher (SMR = 1.31 [95% CI 1.05, 1.65]) than in the general population. The RMR comparing the SMRs in the DCCT conventional versus intensive therapy groups was also significant (RMR = 1.49 [95% CI 1.04, 2.14], P = 0.028). The lower relative mortality in the DCCT intensive therapy compared with conventional therapy group is probably due to residual effects of the differential HbA1c levels during the DCCT, also known as metabolic memory (14,17).

sus intensive therapy groups was also significant (RMR = 1.49 [95% CI 1.04, 2.14], P = 0.028). The lower relative mortality in the DCCT intensive therapy compared with conventional therapy group is probably due to residual effects of the differential HbA1c levels during the DCCT, also known as metabolic memory (14,17). The increased relative mortality in the DCCT conventional versus intensive therapy group was also observed in the secondary intervention cohort (RMR = 1.88 [95% CI 1.13, 3.12], P = 0.0149). Within the primary prevention cohort, the SMR within either group was not significantly different from 1, and the groups did not differ (RMR = 1.17, P = 0.54). Further, whereas mortality in the DCCT/EDIC was significantly higher in males than females, the SMR was similar for both sexes, reflecting the greater mortality among males than females in the general population. Thus, in the DCCT/EDIC cohort with T1D, the excess mortality historically experienced in T1D appears to largely have been erased by intensive therapy. These findings may reflect the reduced occurrence of albuminuria (23). These findings are also consistent with the recent findings from the FinnDiane (24) and Pittsburgh Epidemiology of Diabetes Complications (EDC) (25) studies in which there was no excess mortality compared with the general population in the absence of micro- or greater albuminuria.

educed occurrence of albuminuria (23). These findings are also consistent with the recent findings from the FinnDiane (24) and Pittsburgh Epidemiology of Diabetes Complications (EDC) (25) studies in which there was no excess mortality compared with the general population in the absence of micro- or greater albuminuria. A recent report from Sweden (13), however, reported that an increased mortality risk still persists in T1D, even with glycemic levels at or near those recommended. However, the study collected limited data over only the most recent 8 years of diabetes duration, whereas the cohort had a mean diabetes duration over 20 years at baseline. Every patient had at least one HbA1c measurement, but data on the density or completeness of the HbA1c measurements that comprised their “time updated mean” HbA1c were not provided. Considering the importance of early glycemic control, the conclusion that mortality was two- to threefold higher in patients with diabetes with an HbA1c <7%, compared with the population without diabetes, merits qualification when viewed in a more complete historical perspective. In contrast to the Swedish findings, the overall mortality rates in the DCCT/EDIC cohort were largely similar to the general population. However, increasing levels of HbA1c were strongly associated with increasing mortality risk relative to the general U.S. population, and this was more so among females than males. In the full DCCT/EDIC cohort, a 10% higher HbA1c (e.g., 8.8 vs. 8%) was associated with a 56% higher risk of mortality (14).

ral population. However, increasing levels of HbA1c were strongly associated with increasing mortality risk relative to the general U.S. population, and this was more so among females than males. In the full DCCT/EDIC cohort, a 10% higher HbA1c (e.g., 8.8 vs. 8%) was associated with a 56% higher risk of mortality (14). This relationship between the HbA1c and mortality may represent confounding with other factors or an unhealthy nonadherer effect whereby patients with a very poor HbA1c in both groups may be less adherent to other therapeutic suggestions such as nutrition, physical activity, smoking, and lipid and blood pressure medication adherence. Such confounding could be addressed in a multivariate model to assess the effect of HbA1c on risk when adjusted for markers of adherence. However, EDIC has established a policy that such models will be embargoed until at least 100 subjects from the DCCT conventional therapy group have died, a number that provides adequate power to reliably detect risk factor effects in multivariate models.

e effect of HbA1c on risk when adjusted for markers of adherence. However, EDIC has established a policy that such models will be embargoed until at least 100 subjects from the DCCT conventional therapy group have died, a number that provides adequate power to reliably detect risk factor effects in multivariate models. There are a number of limitations to the current study. Our calculations used the 2013 SMR for the general U.S. population and likely underestimate the rates in the general population in prior years for the relevant follow-up period of 1983–2013. Although these results are consistent with the recent estimate that the life expectancy of childhood-onset diabetes now approaches that of the general population (11), they may not be applicable to the general population or directly comparable to other cohorts with T1D. For example, the DCCT/EDIC cohort has a relatively high socioeconomic status (26), with 55% being professionals on entry (Hollingshead index categories 1 and 2) (27), which might be expected to result in a relatively lower mortality than in the general population of people with T1D.

omparable to other cohorts with T1D. For example, the DCCT/EDIC cohort has a relatively high socioeconomic status (26), with 55% being professionals on entry (Hollingshead index categories 1 and 2) (27), which might be expected to result in a relatively lower mortality than in the general population of people with T1D. There are other important demographic differences between the DCCT/EDIC cohort and populations reported in past studies (4,9,28–30), such as the Allegheny County Registry study that followed children from the time of diabetes onset (9). On entry, DCCT subjects were 13–39 years of age with duration of diabetes 1–15 years. The mean age at the time of diagnosis (21 years) in this cohort is older than the usual mean age of onset and did not include the early mortality related to acute complications, such as hypoglycemia and diabetic ketoacidosis, during childhood (15). Additionally, the Allegheny Registry follow-up began in 1965, whereas the DCCT started in 1983. Furthermore, the DCCT selected participants with a high likelihood of compliance to the treatment protocol and excluded those with hypertension, severe dyslipidemia (15), or other serious comorbidities, thus reducing mortality risk. Interestingly, however, the DCCT conventional therapy group had a similar risk of diabetes complications to that of the Allegheny study (31), which indicates that the low mortality in DCCT/EDIC is not likely to be solely a reflection of the DCCT selection criteria.

r serious comorbidities, thus reducing mortality risk. Interestingly, however, the DCCT conventional therapy group had a similar risk of diabetes complications to that of the Allegheny study (31), which indicates that the low mortality in DCCT/EDIC is not likely to be solely a reflection of the DCCT selection criteria. In conclusion, the long-term follow-up of the DCCT/EDIC T1D cohort shows that the overall mortality in T1D is similar to that of the general population. However, mortality in the DCCT conventional therapy group is somewhat higher than that in the general population. Further, in the overall cohort, relative mortality increases with increasing HbA1c, more prominently among females than males. Clinical trial reg. nos. NCT00360815 and NCT00360893, clinicaltrials.gov. * A complete list of participants in the DCCT/EDIC Research Group is presented in the Supplementary Material published online for the article in N Engl J Med 2015;372:1722–1733. Members of the DCCT/EDIC Writing Group are presented in the appendix. See accompanying article, p. 1309.

Clinical trial reg. nos. NCT00360815 and NCT00360893, clinicaltrials.gov. * A complete list of participants in the DCCT/EDIC Research Group is presented in the Supplementary Material published online for the article in N Engl J Med 2015;372:1722–1733. Members of the DCCT/EDIC Writing Group are presented in the appendix. See accompanying article, p. 1309. Article Information Funding. The DCCT/EDIC has been supported by cooperative agreement grants (1982–1993 and 2012–2017) and contracts (1982–2012) with the Division of Diabetes Endocrinology and Metabolic Diseases of the National Institute of Diabetes and Digestive and Kidney Diseases (current grant numbers U01 DK094176 and U01 DK094157) and by the National Eye Institute, the National Institute of Neurological Disorders and Stroke, the General Clinical Research Centers Program (1993–2007), and the Clinical and Translational Science Center Program (2006 to present), Bethesda, MD.

and Kidney Diseases (current grant numbers U01 DK094176 and U01 DK094157) and by the National Eye Institute, the National Institute of Neurological Disorders and Stroke, the General Clinical Research Centers Program (1993–2007), and the Clinical and Translational Science Center Program (2006 to present), Bethesda, MD. The following industry contributors had no role in the DCCT/EDIC study but provided free or discounted supplies or equipment to support participant adherence to the study: Abbott Diabetes Care (Alameda, CA), Animas (West Chester, PA), Bayer Diabetes Care (North America Headquarters, Tarrytown, NY), Becton Dickinson (Franklin Lakes, NJ), Eli Lilly and Company (Indianapolis, IN), Extend Nutrition (St. Louis, MO), Insulet Corporation (Bedford, MA), LifeScan (Milpitas, CA), Medtronic Diabetes (Minneapolis, MN), Nipro Home Diagnostics (Ft. Lauderdale, FL), Nova Diabetes Care (Billerica, MA), Omron (Shelton, CT), Perrigo Diabetes Care (Allegan, MI), Roche Diabetes Care (Indianapolis, IN), and Sanofi (Bridgewater, NJ). Duality of Interest. No potential conflicts of interest related to this article were reported.

The following industry contributors had no role in the DCCT/EDIC study but provided free or discounted supplies or equipment to support participant adherence to the study: Abbott Diabetes Care (Alameda, CA), Animas (West Chester, PA), Bayer Diabetes Care (North America Headquarters, Tarrytown, NY), Becton Dickinson (Franklin Lakes, NJ), Eli Lilly and Company (Indianapolis, IN), Extend Nutrition (St. Louis, MO), Insulet Corporation (Bedford, MA), LifeScan (Milpitas, CA), Medtronic Diabetes (Minneapolis, MN), Nipro Home Diagnostics (Ft. Lauderdale, FL), Nova Diabetes Care (Billerica, MA), Omron (Shelton, CT), Perrigo Diabetes Care (Allegan, MI), Roche Diabetes Care (Indianapolis, IN), and Sanofi (Bridgewater, NJ). Duality of Interest. No potential conflicts of interest related to this article were reported. Author Contributions. J.M.L. conceived of and designed the study; acquired, analyzed, or interpreted data; drafted the manuscript; critically revised the manuscript for important intellectual content; performed statistical analysis; and obtained funding. I.B. acquired, analyzed, or interpreted data and performed statistical analysis. D.M.N. conceived of and designed the study; acquired, analyzed, or interpreted data; drafted the manuscript; critically revised the manuscript for important intellectual content; obtained funding; and supervised the study. B.Z. conceived of and designed the study; acquired, analyzed, or interpreted data; critically revised the manuscript for important intellectual content; obtained funding; provided administrative, technical, or material support; and supervised the study. D.B. conceived of and designed the study; acquired, analyzed, or interpreted data; drafted the manuscript; and critically revised the manuscript for important intellectual content. J.-Y.C.B. acquired, analyzed, or interpreted data; drafted the manuscript; performed statistical analysis; and provided administrative, technical, or material support. P.C. acquired, analyzed, or interpreted data; drafted the manuscript; performed statistical analysis; provided administrative, technical, or material support; and supervised the study. T.J.O. conceived of and designed the study; acquired, analyzed, or interpreted data; drafted the manuscript; critically revised the manuscript for important intellectual content; performed statistical analysis; obtained funding; provided administrative, technical, or material support; and supervised the study. J.M.L. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

rformed statistical analysis; obtained funding; provided administrative, technical, or material support; and supervised the study. J.M.L. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Appendix Members of the DCCT/EDIC Writing Group: John M. Lachin (George Washington University Biostatistics Center, Rockville, MD), Ionut Bebu (George Washington University Biostatistics Center), David M. Nathan (Massachusetts General Hospital, Harvard Medical School, Boston, MA), Bernard Zinman (Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada), David Brillon (Weill Cornell Medical College, New York, NY), Jye-Yu C. Backlund (George Washington University Biostatistics Center), Patricia Cleary (George Washington University Biostatistics Center), and Trevor J. Orchard (University of Pittsburgh, Pittsburgh, PA).

Introduction Type 1 diabetes has been associated with an increased risk of cardiovascular disease (CVD) morbidity and mortality (1). Despite improvements in risk factor profiles and robust treatment recommendations aimed at preventing diabetes-related complications, CVD remains the leading cause of death among individuals with type 1 diabetes (2,3), and increased risk of CVD is a major health concern. The Diabetes Control and Complications Trial (DCCT) and its follow-up the Epidemiology of Diabetes Interventions and Complications (EDIC) study demonstrated the beneficial effect of intensive therapy on atherosclerosis and major CVD events (2,4–6). The analyses demonstrated that hyperglycemia was a major risk factor for CVD in type 1 diabetes. However, there have been few studies with robust long-term data published evaluating the influence of levels of hyperglycemia in individuals with type 1 diabetes on changes in established CVD risk factors, such as lipids or blood pressure. The DCCT/EDIC study provides the opportunity to explore the interrelationships of traditional CVD risk factors and glycemia in a carefully studied cohort of patients with type 1 diabetes over an extended period of time.

s with type 1 diabetes on changes in established CVD risk factors, such as lipids or blood pressure. The DCCT/EDIC study provides the opportunity to explore the interrelationships of traditional CVD risk factors and glycemia in a carefully studied cohort of patients with type 1 diabetes over an extended period of time. Herein we describe the long-term changes in CVD risk factors observed over a 30-year period of follow-up in the DCCT/EDIC study. The aims are to evaluate the association of glycemic exposure with CVD risk factors and their coprogression, and to describe differences in CVD risk factors between the original DCCT intensive treatment and conventional treatment groups. Delineating the relationship between glycemia and traditional CVD risk factor progression over time may prove beneficial to understanding macrovascular disease in type 1 diabetes as well in providing insight for preventive treatment regimens.

ween the original DCCT intensive treatment and conventional treatment groups. Delineating the relationship between glycemia and traditional CVD risk factor progression over time may prove beneficial to understanding macrovascular disease in type 1 diabetes as well in providing insight for preventive treatment regimens. Research Design and Methods Detailed descriptions of the DCCT intervention and the EDIC observational follow-up study have been published previously (7–9). Briefly, 1,441 subjects with type 1 diabetes were enrolled in the DCCT between 1983 and 1989. Approximately half of the cohort (N = 711) was randomized to receive intensive therapy with a goal of safely maintaining blood glucose levels within a near-normal nondiabetic range. The remainder (N = 730) were assigned to conventional therapy with a goal of clinical well-being and freedom from symptoms related to both hyperglycemia and hypoglycemia. The following two parallel cohorts were recruited: the primary prevention cohort (N = 726), with diabetes duration of 1–5 years, no retinopathy (microaneurysms or worse), and a urine albumin excretion rate (AER) <40 mg/24 h; and the secondary intervention cohort (N = 715), with diabetes duration of 1–15 years, mild to moderate nonproliferative diabetic retinopathy, and an AER of ≤200 mg/24 h. Subjects with a history of CVD or with hypertension (blood pressure >140/90 mmHg or receiving medication) or hyperlipidemia (fasting serum cholesterol level ≥3 SDs above age- and sex-specific means) were not eligible to participate.

rs, mild to moderate nonproliferative diabetic retinopathy, and an AER of ≤200 mg/24 h. Subjects with a history of CVD or with hypertension (blood pressure >140/90 mmHg or receiving medication) or hyperlipidemia (fasting serum cholesterol level ≥3 SDs above age- and sex-specific means) were not eligible to participate. After an average of 6.5 years (range 3–9) of follow-up, 1,422 subjects completed a closeout visit (99% of the original cohort). Subjects who were originally assigned to receive conventional treatment were encouraged to adopt intensive therapy, and subjects in both groups were returned to receive care from their own health care providers. In 1994, 96% of the surviving DCCT cohort enrolled in the EDIC observational study, and after an additional 20 years of follow-up, 1,251 participants (94% of the surviving cohort) continue to be followed. Evaluations Although more frequent medical visits occurred during the DCCT, the present analyses focus only on the data obtained at annual visits during both the DCCT and the EDIC study. In longitudinal analyses, study years 0 through 9 represent the DCCT, and years 10 through 30 the EDIC follow-up study. Owing to staggered entry into the DCCT and the fixed DCCT duration, the numbers evaluated decline over DCCT years 5–9.

ly on the data obtained at annual visits during both the DCCT and the EDIC study. In longitudinal analyses, study years 0 through 9 represent the DCCT, and years 10 through 30 the EDIC follow-up study. Owing to staggered entry into the DCCT and the fixed DCCT duration, the numbers evaluated decline over DCCT years 5–9. Each annual visit included a detailed medical history including demographic and behavioral risk factors, medical outcomes, and a physical examination, which included measurements of height, weight, sitting blood pressure, and pulse rate (7,9). Pulse pressure was defined as the difference between the systolic and diastolic blood pressure readings. Blood samples were collected at each annual visit and were assayed centrally for HbA1c, using high-performance ion-exchange liquid chromatography. Fasting lipids (triglycerides, total, and HDL cholesterol) were measured annually during DCCT and in alternate years during the EDIC study, and were evaluated centrally (10). LDL cholesterol was calculated using the Friedewald equation (11). Concurrent medication usage was collected during the EDIC study, but not during the DCCT. However, the current cardiorenal protective agents were either unavailable (statins, angiotensin receptor blockers) or not prescribed according to protocol (ACE inhibitors) during the DCCT.

ated using the Friedewald equation (11). Concurrent medication usage was collected during the EDIC study, but not during the DCCT. However, the current cardiorenal protective agents were either unavailable (statins, angiotensin receptor blockers) or not prescribed according to protocol (ACE inhibitors) during the DCCT. Classification of CVD Risk Factors For this analysis, risk factors were classified into the following four major categories: protocol dictated (DCCT treatment group, primary prevention vs. secondary intervention cohort); demographic (sex, age, weight, BMI, smoking, drinking alcohol, physical activity, family history of hypertension, myocardial infarction, type 1 and type 2 diabetes); traditional (blood pressure, pulse pressure, pulse rate, total cholesterol, triglycerides, and HDL and LDL cholesterol); and diabetes related (diabetes duration, stimulated C-peptide level, estimated glucose disposal rate, and HbA1c level) (Table 1). Weight and BMI were evaluated separately in men and women. In addition to the current HbA1c value, the DCCT updated mean was used to reflect the cumulative glycemic exposure from baseline up to and including the HbA1c at each visit throughout the DCCT. The DCCT/EDIC study time-weighted arithmetic mean was calculated using the quarterly DCCT values and the annual EDIC study values weighted by 3 and 12 months, respectively. Table 1 Clinical characteristics of the DCCT/EDIC study cohort by treatment group assignment at DCCT baseline (1983–1989) and by the 30th year of DCCT/EDIC study follow-up (2013)

Classification of CVD Risk Factors For this analysis, risk factors were classified into the following four major categories: protocol dictated (DCCT treatment group, primary prevention vs. secondary intervention cohort); demographic (sex, age, weight, BMI, smoking, drinking alcohol, physical activity, family history of hypertension, myocardial infarction, type 1 and type 2 diabetes); traditional (blood pressure, pulse pressure, pulse rate, total cholesterol, triglycerides, and HDL and LDL cholesterol); and diabetes related (diabetes duration, stimulated C-peptide level, estimated glucose disposal rate, and HbA1c level) (Table 1). Weight and BMI were evaluated separately in men and women. In addition to the current HbA1c value, the DCCT updated mean was used to reflect the cumulative glycemic exposure from baseline up to and including the HbA1c at each visit throughout the DCCT. The DCCT/EDIC study time-weighted arithmetic mean was calculated using the quarterly DCCT values and the annual EDIC study values weighted by 3 and 12 months, respectively. Table 1 Clinical characteristics of the DCCT/EDIC study cohort by treatment group assignment at DCCT baseline (1983–1989) and by the 30th year of DCCT/EDIC study follow-up (2013) DCCT baseline (1983–1989) Average over DCCT/EDIC Intensive treatment(N = 711) Conventional treatment(N = 730) P value* Intensive treatment(N = 711) Conventional treatment(N = 730) P value* Protocol Cohort (% primary prevention)† 49 52 0.2818 Demographic Physical Sex (% women)† 49 46 0.3169 Age (years)‡ 27 ± 7 27 ± 7 0.1383 41 ± 8 40 ± 8 0.0141 Adult (%)† 87 86 0.5162 Weight (kg)‖ Men 73.8 ± 10.8 75.8 ± 11.7 0.0091 89.0 ± 10.6 83.9 ± 10.8 <0.0001 Women 62.7 ± 8.6 62.1 ± 9.5 0.2966 74.3 ± 9.5 70.4 ± 9.4 <0.0001 BMI (kg/m2)‖ Men 23.4 ± 2.6 23.9 ± 2.9 0.0045 27.7 ± 2.8 26.6 ± 2.9 <0.0001 Women 23.3 ± 2.8 22.9 ± 2.9 0.0610 27.3 ± 3.2 26.0 ± 3.2 <0.0001 Behavioral Current cigarette smoker (%) 19 18 0.9718 17 17 0.9982 Occasional or regular drinker (%) 21 22 0.4852 39 41 0.3492 Moderate or strenuous activity (%) 70 69 0.6880 56 56 0.9455 Family history Family history (%)† Hypertension 57 56 0.8445 Myocardial infarction 48 49 0.6459 Type 1 diabetes 14 14 0.8025 Type 2 diabetes 10 8 0.4238 Traditional Blood pressure Systolic (mmHg) 113 ± 12 115 ± 12 0.0116 119 ± 8 119 ± 9 0.4321 Diastolic (mmHg) 72 ± 9 73 ± 9 0.2574 74 ± 5 74 ± 5 0.7303 Pulse pressure (mmHg) 41 ± 10 42 ± 10 0.0639 45 ± 6 45 ± 6 0.3492 Pulse rate Rate (bpm) 76 ± 11 76 ± 11 0.7269 72 ± 7 73 ± 7 0.0094 Lipid Total cholesterol (mg/dL) 177 ± 33 176 ± 34 0.5289 181 ± 25 183 ± 25 0.1216 Triglycerides (mg/dL) 81 ± 43 82 ± 51 0.8151 72 ± 27 77 ± 27 0.0002 HDL cholesterol (mg/dL) 51 ± 12 50 ± 12 0.5048 56 ± 11 56 ± 11 0.7699 LDL cholesterol (mg/dL) 110 ± 29 109 ± 29 0.4967 108 ± 22 109 ± 22 0.2948 Diabetes related History Duration of diabetes (years)‡ 6 ± 4 5 ± 4 0.1441 20 ± 5 19 ± 5 0.0108 Stimulated C-peptide (nmol/L)†¶ Duration <60 months 0.16 ± 0.13 0.16 ± 0.13 0.5482 Duration ≥60 months 0.04 ± 0.03 0.04 ± 0.04 0.0689 eGDR† 7.4 ± 1.8 7.3 ± 1.9 0.3798 Glycemia HbA1c (%) 9.1 ± 1.6 9.1 ± 1.6 0.5542 7.8 ± 1.0 8.5 ± 1.0 <0.0001 HbA1c (mmol/mol) 75.8 ± 17.4 75.5 ± 17.9 61.5 ± 10.4 69.0 ± 10.5 Data are reported as the mean ± SD or %. eGDR, estimated glucose disposal rate. Boldface type indicates P values that are significant at the P < 0.05 level.

4 ± 1.8 7.3 ± 1.9 0.3798 Glycemia HbA1c (%) 9.1 ± 1.6 9.1 ± 1.6 0.5542 7.8 ± 1.0 8.5 ± 1.0 <0.0001 HbA1c (mmol/mol) 75.8 ± 17.4 75.5 ± 17.9 61.5 ± 10.4 69.0 ± 10.5 Data are reported as the mean ± SD or %. eGDR, estimated glucose disposal rate. Boldface type indicates P values that are significant at the P < 0.05 level. *At DCCT baseline, treatment group comparisons were made using the Wilcoxon rank sum test or the χ2 test. For characteristics repeated over time (e.g., weight or percentage of smokers), the average mean or prevalence over 30 years of DCCT/EDIC study annual follow-up was computed using longitudinal generalized estimating equations (GEEs) for repeated measures. SDs were estimated from the SEs using the following equation, SD = SE*SQRT(N). The SDs are smaller during the DCCT/EDIC study follow-up period owing to the larger amount of information in the longitudinal models. †Cohort, sex, adult, and family history are fixed baseline characteristics. C-peptide level data were not collected during the EDIC study. Waist data used to calculate eGDR were not measured during the DCCT. ‡Age and duration were not evaluated using longitudinal GEE models because each is a function of time itself. Instead, the average age and duration were computed for each subject over that subject’s length of follow-up. The average mean value over all subjects and its SD are presented. Treatment group comparisons were made using the Wilcoxon rank sum test.

sing longitudinal GEE models because each is a function of time itself. Instead, the average age and duration were computed for each subject over that subject’s length of follow-up. The average mean value over all subjects and its SD are presented. Treatment group comparisons were made using the Wilcoxon rank sum test. ‖Data for men were based on 366 intensive treatment and 395 conventional treatment participants; data for women were based on 345 intensive treatment and 335 conventional treatment participants. ¶Data for duration <60 months based on 412 intensive treatment and 443 conventional treatment participants; data for duration of ≥60 months based on 299 intensive treatment and 287 conventional treatment participants.

‖Data for men were based on 366 intensive treatment and 395 conventional treatment participants; data for women were based on 345 intensive treatment and 335 conventional treatment participants. ¶Data for duration <60 months based on 412 intensive treatment and 443 conventional treatment participants; data for duration of ≥60 months based on 299 intensive treatment and 287 conventional treatment participants. Statistical Analyses At DCCT baseline, quantitative and categorical characteristics were compared between treatment groups using the Wilcoxon rank sum test and χ2 test, respectively. Generalized linear mixed models were used to assess covariate effects on the mean of each quantitative risk factor over repeated time points, and generalized estimating equation models were used to assess effects on the prevalence of each binomial risk factor. The DCCT/EDIC study year (time 0–9 years representing the DCCT, and 10–30 years representing the EDIC study) was included as a class effect. The models assumed an unstructured covariance structure, or, in cases where the model did not converge, a heterogeneous compound symmetry structure. Covariates measured repeatedly over time entered the models as time-dependent covariates. Pearson correlation coefficients were used to evaluate the associations among each of the protocol-dictated, demographic, traditional, and diabetes-related risk factors at the DCCT baseline. Additionally, a comprehensive analysis of collinearity was completed (12).

time entered the models as time-dependent covariates. Pearson correlation coefficients were used to evaluate the associations among each of the protocol-dictated, demographic, traditional, and diabetes-related risk factors at the DCCT baseline. Additionally, a comprehensive analysis of collinearity was completed (12). The signed t statistic was used as a measure of the magnitude and direction of the association between an outcome and a covariate. Models were fit without HbA1c level and then by simultaneously adjusting for HbA1c level as a time-dependent covariate in order to evaluate the mediating effect of HbA1c level. All analyses were performed using SAS software (version 9.3; SAS Institute, Cary, NC). A two-sided P value ≤0.05 was considered to be statistically significant.

ithout HbA1c level and then by simultaneously adjusting for HbA1c level as a time-dependent covariate in order to evaluate the mediating effect of HbA1c level. All analyses were performed using SAS software (version 9.3; SAS Institute, Cary, NC). A two-sided P value ≤0.05 was considered to be statistically significant. Results Participant Characteristics The characteristics of the DCCT/EDIC study participants at baseline and after 30 years of follow-up are presented in Table 1. There were no major differences between the intensive treatment and conventional treatment groups at DCCT baseline, except for a 2 mmHg higher systolic blood pressure and a 2-kg higher weight in men in the conventional group. Over the course of the entire 30-year study period, subjects in the conventional treatment group had a higher overall mean pulse rate (73 ± 7 vs. 72 ± 7 bpm, P = 0.0094) over all visits combined, a higher triglyceride level (77 ± 27 vs. 72 ± 27 mg/dL, P = 0.0002), and higher HbA1c level (8.5 ± 1.0% vs. 7.8 ± 1.0%, P < 0.0001). The difference in mean HbA1c level was largely accounted for by the lower HbA1c level maintained by design in the intensive treatment group during the DCCT. Men and women in the intensive treatment group had a 5- and 4-kg higher mean weight, respectively, over the duration of the study compared with conventionally treated men and women (P < 0.0001).

vel was largely accounted for by the lower HbA1c level maintained by design in the intensive treatment group during the DCCT. Men and women in the intensive treatment group had a 5- and 4-kg higher mean weight, respectively, over the duration of the study compared with conventionally treated men and women (P < 0.0001). There were strong correlations between systolic and diastolic blood pressure, and between total and LDL cholesterol values (data not shown). Thus, the subsequent risk factor models did not include total cholesterol. Other pairs of variables such as diabetes duration/cohort (primary prevention vs. secondary intervention) and weight/BMI were highly correlated by definition. However, a test of collinearity did not identify any concerns. Long-term Changes in Risk Factors Figure 1 presents the mean ± SE for each of the quantitative risk factors over time along with the prevalence of any relevant medication use during EDIC study. During the DCCT, there was a substantially greater increase in weight in the intensive versus conventional treatment group, and more so among women (Fig. 1). This group difference in weight among women persisted during the EDIC study, whereas there was a negligible group difference among men in the EDIC study.

C study. During the DCCT, there was a substantially greater increase in weight in the intensive versus conventional treatment group, and more so among women (Fig. 1). This group difference in weight among women persisted during the EDIC study, whereas there was a negligible group difference among men in the EDIC study. Figure 1 Body weight and hemodynamic measures during the DCCT/EDIC study by original assignment to intensive or conventional treatment during the DCCT. Data are reported as the mean ± SE at each DCCT/EDIC study follow-up year (black lines, conventional treatment; gray lines, intensive treatment). The average mean values over time are presented in Table 1. The panels for pulse pressure and pulse rate also present the proportion of subjects receiving concurrent medication.

a are reported as the mean ± SE at each DCCT/EDIC study follow-up year (black lines, conventional treatment; gray lines, intensive treatment). The average mean values over time are presented in Table 1. The panels for pulse pressure and pulse rate also present the proportion of subjects receiving concurrent medication. Systolic blood pressure increased steadily over the 30-year period, while the diastolic blood pressure rose during the first 17 years and began to fall thereafter (Fig. 1). The pulse pressure (systolic − diastolic) also increased from a mean of 42 mmHg at DCCT baseline to 52 mmHg by year 30, mainly due to the decrease in diastolic blood pressure beyond year 17 rather than to the increase in systolic blood pressure (Fig. 1). This was accompanied by an increasing prevalence of antihypertensive medication use during EDIC study (6% at year 10 to 60% by year 30). Figure 1 also shows an increasing pulse rate (after an initial dip in year 2) that persisted until year 7–8 of the DCCT, before declining during the last years of the DCCT and throughout the EDIC study. The latter may reflect the increasing use of β-blockers (1% at year 10 to 14% by year 30). Notably, a slightly higher pulse rate was observed in the conventional treatment group compared with the intensive treatment group throughout most of the DCCT/EDIC study follow-up years.

of the DCCT and throughout the EDIC study. The latter may reflect the increasing use of β-blockers (1% at year 10 to 14% by year 30). Notably, a slightly higher pulse rate was observed in the conventional treatment group compared with the intensive treatment group throughout most of the DCCT/EDIC study follow-up years. Compared with participants in the conventional group, those in the intensive treatment group had numerically lower LDL cholesterol and triglyceride levels during the DCCT (Fig. 2). The pattern became somewhat reversed during the EDIC study, although both groups experienced decreasing LDL cholesterol levels from year 12 onward as the use of lipid-lowering medication increased (2% at year 10 to 62% by year 30). Overall, serum triglyceride levels were remarkably stable throughout the DCCT/EDIC study (Fig. 2). There were no treatment group differences in HDL cholesterol levels: the levels were stable throughout the DCCT and increased by 24% by year 30 in the EDIC study (Supplementary Fig. 1). Figure 2 Lipid profile and glycemic control during the DCCT/EDIC study by original assignment to intensive or conventional treatment during the DCCT. Data are reported as the mean ± SE at each DCCT/EDIC study follow-up year (black lines, conventional treatment; gray lines, intensive treatment). The average mean values over time are presented in Table 1. The panel for LDL cholesterol also presents the proportion of subjects receiving concurrent medication. Triglyceride values were log transformed, and the geometric means are presented.

ear (black lines, conventional treatment; gray lines, intensive treatment). The average mean values over time are presented in Table 1. The panel for LDL cholesterol also presents the proportion of subjects receiving concurrent medication. Triglyceride values were log transformed, and the geometric means are presented. Although the current HbA1c levels in the intensive and conventional treatment groups came together at the beginning of the EDIC study follow-up period, the DCCT/EDIC study time-weighted mean HbA1c values remained significantly higher in the conventional treatment group over the 20 years of the EDIC study follow-up (Fig. 2). Association of Diabetes-Related Risk Factors With Progression of Traditional CVD Risk Factors Table 2 presents the association of treatment group and HbA1c level as a time-dependent covariate with the traditional CVD risk factors in the general population, with adjustment only for age, primary versus secondary cohort, and sex when appropriate. The regression coefficient (mean difference between groups or slope for a quantitative predictor), SE, and P value are also shown. Table 2 Longitudinal association of treatment group and HbA1c with traditional cardiovascular risk factors during the DCCT/EDIC study, minimally adjusted for other factors

Association of Diabetes-Related Risk Factors With Progression of Traditional CVD Risk Factors Table 2 presents the association of treatment group and HbA1c level as a time-dependent covariate with the traditional CVD risk factors in the general population, with adjustment only for age, primary versus secondary cohort, and sex when appropriate. The regression coefficient (mean difference between groups or slope for a quantitative predictor), SE, and P value are also shown. Table 2 Longitudinal association of treatment group and HbA1c with traditional cardiovascular risk factors during the DCCT/EDIC study, minimally adjusted for other factors Traditional cardiovascular risk factors BMI SystolicBP (mmHg) DiastolicBP(mmHg) Pulse pressure(mmHg) Pulse rate (bpm) Triglycerides (mg/dL)* HDL cholesterol(mg/dL) LDL cholesterol(mg/dL) Men(kg/m2) Women (kg/m2) Intensive vs. conventional 0.82 ± 0.12 1.30 ± 0.15 0.24 ± 0.37 0.22 ± 0.25 0.18 ± 0.26 −0.93 ± 0.33 −6.44 ± 1.71 −0.09 ± 0.47 −1.92 ± 1.10 <0.0001 <0.0001 0.5196 0.3722 0.4894 0.0055 0.0003 0.8489 0.0808 (6.87) (8.76) (0.64) (0.89) (0.69) (−2.78) (−3.64) (−0.19) (−1.75) Current HbA1c level (%)† −0.30 ± 0.01 −0.19 ± 0.01 −0.38 ± 0.06 0.18 ± 0.04 −0.48 ± 0.05 0.31 ± 0.05 5.77 ± 0.21 0.12 ± 0.05 3.36 ± 0.13 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0075 <0.0001 (−26.61) (−14.58) (–6.46) (4.32) (−9.91) (6.38) (27.66) (2.67) (26.17) DCCT/EDIC study time-weighted HbA1c level (%)† −0.40 ± 0.02 −0.13 ± 0.02 −0.09 ± 0.10 0.22 ± 0.07 −0.12 ± 0.08 1.17 ± 0.09 8.15 ± 0.34 −0.05 ± 0.07 3.63 ± 0.20 <0.0001 <0.0001 0.3806 0.0018 0.1218 <0.0001 <0.0001 0.4595 <0.0001 (−22.02) (−6.12) (−0.88) (3.13) (−1.55) (13.75) (24.60) (−0.74) (17.99) DCCT updated HbA1c level (%)† −0.33 ± 0.02 −0.09 ± 0.03 −0.15 ± 0.10 0.10 ± 0.07 −0.12 ± 0.08 1.02 ± 0.09 5.86 ± 0.39 −0.08 ± 0.09 2.65 ± 0.23 <0.0001 0.0006 0.1348 0.1563 0.1224 <0.0001 <0.0001 0.3855 <0.0001 (−14.35) (−3.45) (−1.50) (1.42) (−1.55) (11.62) (15.48) (−0.87) (11.41) Each cell represents a single generalized linear mixed model adjusting for baseline age, primary prevention vs. secondary intervention group, and sex when appropriate. Data are β-estimates ± SE and P values (t statistics). β-Estimates are equal to the mean difference between groups or the slope of the association. The signed t statistic corresponds to the magnitude and directionality of the association. BP, blood pressure.

n vs. secondary intervention group, and sex when appropriate. Data are β-estimates ± SE and P values (t statistics). β-Estimates are equal to the mean difference between groups or the slope of the association. The signed t statistic corresponds to the magnitude and directionality of the association. BP, blood pressure. *Triglyceride values were log transformed and the percentage change in triglyceride levels per 1-unit change in predictor is shown [100(eβ − 1)] ± [100(eβ*SE)]. †Each predictor is entered into the model as a time-dependent covariate. The DCCT updated mean is defined as the cumulative glycemic exposure from baseline up to and including the HbA1c level at each visit through DCCT. The DCCT/EDIC study time-weighted mean was calculated using the quarterly DCCT and annual EDIC study values weighted by 3 and 12 months, respectively.

e-dependent covariate. The DCCT updated mean is defined as the cumulative glycemic exposure from baseline up to and including the HbA1c level at each visit through DCCT. The DCCT/EDIC study time-weighted mean was calculated using the quarterly DCCT and annual EDIC study values weighted by 3 and 12 months, respectively. Participants in the intensive treatment group had a significantly higher BMI, lower pulse rate, and lower triglyceride levels. The strongest longitudinal associations were among the lipid measurements and glycemia, with higher current HbA1c levels being strongly associated with increases in triglyceride and LDL cholesterol levels. A higher current HbA1c level was also associated with decreases in BMI, systolic blood pressure, and pulse pressure. Similar associations were observed for the DCCT/EDIC study time-weighted mean HbA1c level as well as for the DCCT updated mean HbA1c level. The magnitude and direction for each comparison in Table 2 remained the same after further adjustment for corresponding medications (e.g., antihypertensive medication for treatment of blood pressure, β-blockers for pulse rate; and lipid-lowering medications for triglyceride, and HDL and LDL cholesterol levels).

level. The magnitude and direction for each comparison in Table 2 remained the same after further adjustment for corresponding medications (e.g., antihypertensive medication for treatment of blood pressure, β-blockers for pulse rate; and lipid-lowering medications for triglyceride, and HDL and LDL cholesterol levels). Supplementary Table 1 extends the analyses in Table 2 to include the association of all baseline and time-dependent predictors with each traditional CVD risk factor, with the signed t statistic to show the significance and direction of the partial association of each covariate individually (without minimal adjustments). These analyses demonstrate robust associations of numerous CVD risk factors including age, weight, smoking, physical activity, blood pressure, heart rate, and lipid values. Time-averaged triglyceride levels had a robust inverse association with HDL cholesterol levels, and positive associations with BMI, blood pressure, pulse rate, and LDL cholesterol values. Family history of hypertension was associated with blood pressure, and family history of type 2 diabetes was weakly associated with triglyceride and LDL cholesterol levels. There was no discernible association between duration of diabetes and traditional CVD risk factors, except for increased pulse pressure (secondary to a decrease in diastolic blood pressure).

ed with blood pressure, and family history of type 2 diabetes was weakly associated with triglyceride and LDL cholesterol levels. There was no discernible association between duration of diabetes and traditional CVD risk factors, except for increased pulse pressure (secondary to a decrease in diastolic blood pressure). Influence of Glycemia For each significant treatment group association in Table 2, the potential mediating effect of glycemia was evaluated. The significant treatment group differences in pulse rate and triglyceride level were attenuated after adjustment for current HbA1c level (P = 0.0947 and P = 0.2876, respectively), whereas the significant association between BMI and treatment group remained largely unaffected by current HbA1c values (data not shown). In additional models, the DCCT/EDIC study time-weighted and DCCT updated mean HbA1c values did not mediate any of the significant treatment group associations originally observed in Table 2. As a result, current HbA1c level was used in all of the subsequent multivariate models.

by current HbA1c values (data not shown). In additional models, the DCCT/EDIC study time-weighted and DCCT updated mean HbA1c values did not mediate any of the significant treatment group associations originally observed in Table 2. As a result, current HbA1c level was used in all of the subsequent multivariate models. Multivariate Associations With Progression of Risk Factors Supplementary Table 2 presents the association of each covariate in a multivariate model adjusted for the current HbA1c level and all other factors, and Table 3 summarizes these associations for treatment group. There were no significant treatment group differences in the jointly adjusted models at the P < 0.01 level with the exception of BMI, which remained significantly higher in females in the intensive treatment group, even after adjusting for all other covariates (Table 3). Current HbA1c level was associated with all CVD risk factors, excluding HDL cholesterol level. Table 3 Longitudinal associations of treatment group and HbA1c level with traditional cardiovascular risk factors during the DCCT/EDIC study, fully adjusted for all other factors

Multivariate Associations With Progression of Risk Factors Supplementary Table 2 presents the association of each covariate in a multivariate model adjusted for the current HbA1c level and all other factors, and Table 3 summarizes these associations for treatment group. There were no significant treatment group differences in the jointly adjusted models at the P < 0.01 level with the exception of BMI, which remained significantly higher in females in the intensive treatment group, even after adjusting for all other covariates (Table 3). Current HbA1c level was associated with all CVD risk factors, excluding HDL cholesterol level. Table 3 Longitudinal associations of treatment group and HbA1c level with traditional cardiovascular risk factors during the DCCT/EDIC study, fully adjusted for all other factors Traditional cardiovascular risk factors BMI SystolicBP(mmHg) DiastolicBP(mmHg) Pulse pressure(mmHg) Pulse rate(bpm) Triglycerides (mg/dL)* HDL cholesterol(mg/dL) LDL cholesterol(mg/dL) Men(kg/m2) Women (kg/m2) Intensive vs. conventional −0.17 ± 0.14 1.01 ± 0.17 −0.46 ± 0.30 0.34 ± 0.19 −0.50 ± 0.29 −1.33 ± 0.51 0.03 ± 1.69 0.82 ± 0.50 1.80 ± 1.03 0.2190 <0.0001 0.1227 0.0729 0.0872 0.0100 0.9873 0.0997 0.0820 (−1.23) (5.93) (−1.54) (1.79) (−1.71) (−2.58) (0.02) (1.65) (1.74) Current HbA1c level (%)† −0.35 ± 0.02 −0.25 ± 0.02 −0.37 ± 0.06 0.22 ± 0.04 −0.39 ± 0.06 0.33 ± 0.06 5.49 ± 0.23 0.02 ± 0.05 2.99 ± 0.14 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.6538 <0.0001 (−21.61) (−14.43) (−6.49) (5.32) (−6.62) (5.89) (24.77) (0.45) (21.04) Each column represents a single generalized linear mixed model jointly adjusted for all other baseline and time-dependent predictors listed in Supplementary Table 2. Data are reported as β-estimates ± SE and P values (t statistics). β-Estimates are equal to the mean difference between groups or the slope of the association. The signed t statistic corresponds to the magnitude and directionality of the association. BP, blood pressure.

ndent predictors listed in Supplementary Table 2. Data are reported as β-estimates ± SE and P values (t statistics). β-Estimates are equal to the mean difference between groups or the slope of the association. The signed t statistic corresponds to the magnitude and directionality of the association. BP, blood pressure. *Triglyceride values were log transformed and the percentage change in triglycerides per 1-unit change in predictor is shown [100(eβ − 1)] ± [100(eβ*SE)]. †Each predictor is entered into the model as a time-dependent covariate. Compared with the results shown in Supplementary Table 1, there were fewer significant associations shown in Supplementary Table 2 after adjustment for all other factors. Nevertheless, current HbA1c level persisted as a significant predictor of the longitudinal changes in all of the CVD risk factors (with the exception of HbA1c level with HDL cholesterol level). Each jointly adjusted regression model accounted for >85% of the variation in the response variables.

t for all other factors. Nevertheless, current HbA1c level persisted as a significant predictor of the longitudinal changes in all of the CVD risk factors (with the exception of HbA1c level with HDL cholesterol level). Each jointly adjusted regression model accounted for >85% of the variation in the response variables. Longitudinal changes in male and female BMI were associated with similar risk factors, including smoking, blood pressure, and triglyceride, LDL cholesterol, and current HbA1c levels (Supplementary Table 2). An increase in male BMI was also associated with older age. Not surprisingly, the systolic blood pressure was the strongest correlate of diastolic blood pressure, and diastolic blood pressure was the strongest correlate of systolic blood pressure. Systolic blood pressure and pulse pressure were both associated with gender, age, pulse rate, and current HbA1c level. The longitudinal changes in HDL cholesterol level were highly influenced by behavioral factors, including smoking and drinking alcohol, whereas triglyceride and LDL cholesterol levels were associated with blood pressure, pulse rate, and HbA1c level.

both associated with gender, age, pulse rate, and current HbA1c level. The longitudinal changes in HDL cholesterol level were highly influenced by behavioral factors, including smoking and drinking alcohol, whereas triglyceride and LDL cholesterol levels were associated with blood pressure, pulse rate, and HbA1c level. Conclusions In previous reports (2,4–6), we demonstrated the beneficial effect of intensive diabetes management on atherosclerosis and the occurrence of clinical cardiovascular events among participants in the DCCT/EDIC study. These reports also demonstrated that hyperglycemia is a risk factor for CVD in individuals with type 1 diabetes. However, it is not known to what extent glycemic exposure influences the magnitude and direction of long-term changes in established CVD risk factors among patients with type 1 diabetes. The ongoing long-term follow-up of the DCCT/EDIC study cohort provides an opportunity to answer this question.

with type 1 diabetes. However, it is not known to what extent glycemic exposure influences the magnitude and direction of long-term changes in established CVD risk factors among patients with type 1 diabetes. The ongoing long-term follow-up of the DCCT/EDIC study cohort provides an opportunity to answer this question. In the present report, we have examined the coprogression of CVD risk factors and their interactions with glycemic exposure among DCCT/EDIC study participants over a 30-year period of follow-up. Although age is a major risk factor for an increased risk of clinical CVD events in the general population, it was not strongly associated with increases in many risk factors in the DCCT/EDIC study cohort, with the exception of a positive association with BMI in men, systolic (but not diastolic) blood pressure, and pulse pressure. Likewise, sex was not strongly associated with lipid profile. These results suggest that the well-known influence of age and sex on clinical CVD risk in patients with type 1 diabetes is not predominantly driven by their effects on traditional risk factors. The lack of an association between the duration of diabetes and traditional CVD risk factors (notably, blood pressure and lipid levels) should be interpreted with caution, as an increasing proportion of participants received medications for the control of hypertension and dyslipidemia during the EDIC study period.

ors. The lack of an association between the duration of diabetes and traditional CVD risk factors (notably, blood pressure and lipid levels) should be interpreted with caution, as an increasing proportion of participants received medications for the control of hypertension and dyslipidemia during the EDIC study period. Although ambient HbA1c levels in the intensive and conventional group came together at the beginning of the EDIC study follow-up period, the DCCT/EDIC study time-weighted mean HbA1c values continue to be significantly higher in the conventional group. We found that the strongest longitudinal associations were among the current HbA1c levels and lipid measurements, although other significant associations also emerged. The strong association among higher current HbA1c levels and higher triglyceride and LDL cholesterol levels is consistent with the known effect of poorly controlled diabetes on lipid metabolism (13). Triglyceride concentration during the DCCT years, when glycemic control differed markedly between groups, was lower among subjects in the intensive treatment group, despite their higher weight gain, which reflects the impact of intensive insulin therapy and improved glycemic control in regulating triglyceride levels.

. Triglyceride concentration during the DCCT years, when glycemic control differed markedly between groups, was lower among subjects in the intensive treatment group, despite their higher weight gain, which reflects the impact of intensive insulin therapy and improved glycemic control in regulating triglyceride levels. The robust association of current HbA1c level with blood pressure and heart rate is concordant with known clinical associations between diabetes and hypertension, and is likely mediated by autonomic mechanisms (14,15). The pulse pressure of study participants has widened progressively (from ∼40 to >50 mmHg) during the nearly 30-year follow-up period. Because a wide pulse pressure range may be a stronger predictor of heart disease than blood pressure, the latter observation is of some concern (16). The traditional etiology of elevated pulse pressure is arterial stiffness, as occurs in aging, atherosclerosis, and diabetes. In this study cohort, the increasing pulse pressure resulted from a combination of rising systolic blood pressure with relatively level diastolic blood pressure during the DCCT period to EDIC study year 17, and decreasing diastolic blood pressure with stable systolic blood pressure from EDIC study year 17 onward. Among subjects without diabetes, systolic blood pressure tends to increase progressively with age, and diastolic blood pressure also rises with age until ∼60 years of age, and then decreases thereafter, most likely due to arterial stiffness and decreased vascular compliance (17). Notably, the proportion of patients receiving antihypertensive treatment increased from 6% at year 10 to 60% at year 30. The effective treatment of hypertension usually also restores pulse pressure toward more normal values. Thus, the persistent widening of the pulse pressure is not fully explained by exposure to antihypertensive agents, and could well be related to diastolic dysfunction and accelerated arterial aging associated with diabetes (17–19).

treatment of hypertension usually also restores pulse pressure toward more normal values. Thus, the persistent widening of the pulse pressure is not fully explained by exposure to antihypertensive agents, and could well be related to diastolic dysfunction and accelerated arterial aging associated with diabetes (17–19). The negative association between current HbA1c levels and BMI could be due to glycosuria-induced weight loss secondary to poorly controlled diabetes. Other interesting observations in the longitudinal cohort include corroboration of several physiologically congruent interactions, as follows: weight was predictive of blood pressure, heart rate, and lipid profile; smoking was associated with lower BMI, lower HDL cholesterol level, and higher heart rate, and triglyceride and LDL cholesterol levels (20–22); and physical activity was associated with lower BMI, heart rate, and triglyceride levels, and higher HDL cholesterol levels.

e of blood pressure, heart rate, and lipid profile; smoking was associated with lower BMI, lower HDL cholesterol level, and higher heart rate, and triglyceride and LDL cholesterol levels (20–22); and physical activity was associated with lower BMI, heart rate, and triglyceride levels, and higher HDL cholesterol levels. The present report has several strengths, including the fact that the data were obtained from a well-documented population that has been observed for 30 years. Previous reports from the DCCT/EDIC study (23) have established an association between blood pressure and AER that was significantly modified by treatment group and glycemia. The current study extends that observation by assessing the interaction of time-averaged glycemic exposure with an array of clinical, biochemical, and biobehavioral CVD risk factors. The findings indicate that these risk factors are significantly interrelated and coprogress in a time-dependent manner. The demonstration of strong longitudinal associations among HbA1c level and traditional CVD risk factors argues strongly for a clinical directive to optimize control of blood pressure, dyslipidemia, and hyperglycemia in the management of patients with type 1 diabetes (24).

ted and coprogress in a time-dependent manner. The demonstration of strong longitudinal associations among HbA1c level and traditional CVD risk factors argues strongly for a clinical directive to optimize control of blood pressure, dyslipidemia, and hyperglycemia in the management of patients with type 1 diabetes (24). The DCCT/EDIC study has established that the updated weighted mean HbA1c level over the DCCT and the EDIC study combined is a stronger determinant of the risk of progression of complications over time than is the current HbA1c value. However, herein, the current HbA1c value has a stronger association with the current value of other risk factors than does the updated mean HbA1c. This indicates that the current HbA1c value has a short-term association with these other risk factors. It would also be expected that the updated mean HbA1c level would have a stronger association with the updated mean of these CVD risk factors. Among the limitations of the study, the exclusively type 1 diabetes cohort and the lack of ethnic diversity (96% non-Hispanic white) diminish the generalizability of the findings. Also, study participants had a mean BMI of <24 kg/m2 at study enrollment and ∼27 kg/m2 averaged over the DCCT/EDIC study period, which is not representative of the current predominantly overweight U.S. general population. Furthermore, by focusing on the interactions among glycemic and nonglycemic predictors of traditional CVD risk factors, the present report does not consider the possible contribution of nontraditional risk factors.

tudy period, which is not representative of the current predominantly overweight U.S. general population. Furthermore, by focusing on the interactions among glycemic and nonglycemic predictors of traditional CVD risk factors, the present report does not consider the possible contribution of nontraditional risk factors. In conclusion, we have reported the longitudinal coprogression of glycemic and nonglycemic predictors of traditional CVD risk factors during an extensive, ∼30-year follow-up of the DCCT/EDIC study type 1 diabetes cohort. The interrelationships we observed among the predictors and the CVD risk factors are pathophysiologically congruent, and are in the same direction as prior observations based on the more definitive clinical CVD events. Over time, there were significant treatment group differences in a number of CVD risk factors and substantial associations with measures of HbA1c. Although the significant association with current HbA1c level dominated, it did not completely mediate the treatment group differences for all factors. The greater understanding of the relationships among diabetes-related risk factors and established CVD risk factors may provide insight into the design of individualized comprehensive interventions for the control of comorbidities and the reduction of CVD risk in persons with type 1 diabetes.

fferences for all factors. The greater understanding of the relationships among diabetes-related risk factors and established CVD risk factors may provide insight into the design of individualized comprehensive interventions for the control of comorbidities and the reduction of CVD risk in persons with type 1 diabetes. Appendix Writing Group for the DCCT/EDIC Research Group. The members of the Writing Group for the DCCT/EDIC Research Group are as follows: Barbara H. Braffett, Ionut Bebu, and John M. Lachin (The Biostatistics Center, George Washington University, Rockville, MD); Samuel Dagogo-Jack (Division of Endocrinology, Diabetes and Metabolism, University of Tennessee Health Science Center, Memphis, TN); Mary Larkin (Massachusetts General Hospital Diabetes Center, Harvard Medical School, Boston, MA); William Sivitz (Department of Internal Medicine, Division of Endocrinology and Metabolism, University of Iowa, Iowa City, IA); Orville Kolterman (University of California, San Diego, La Jolla, CA); and Saul Genuth (Case-Western Reserve University, Cleveland, OH). Clinical trial reg. nos. NCT00360815 and NCT00360893, clinicaltrials.gov. This article contains Supplementary Data online at http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc16-0502/-/DC1. * A list of the members of The Writing Group for the DCCT/EDIC Research Group appears in the APPENDIX and a complete list of participants in the DCCT/EDIC Research Group is presented in the Supplementary Appendix to the article in N Engl J Med 2015;372:1722–1733.

This article contains Supplementary Data online at http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc16-0502/-/DC1. * A list of the members of The Writing Group for the DCCT/EDIC Research Group appears in the APPENDIX and a complete list of participants in the DCCT/EDIC Research Group is presented in the Supplementary Appendix to the article in N Engl J Med 2015;372:1722–1733. Article Information Funding. The DCCT/EDIC study has been supported by cooperative agreement grants (1982–1993, 2012–2017) and contracts (1982–2012) with the Division of Diabetes Endocrinology and Metabolic Diseases of the National Institute of Diabetes and Digestive and Kidney Diseases (current grant numbers U01-DK-094176 and U01-DK-094157) and through support by the National Eye Institute, the National Institute of Neurological Disorders and Stroke, the General Clinical Research Centers Program (1993–2007), and Clinical Translational Science Center Program (2006 to present), Bethesda, MD. Industry contributors have had no role in the DCCT/EDIC study but have provided free or discounted supplies or equipment to support participants’ adherence to the study, as follows: Abbott Diabetes Care (Alameda, CA), Animas (Westchester, PA), Bayer Diabetes Care (North American Headquarters, Tarrytown, NY), Becton Dickinson (Franklin Lakes, NJ), Eli Lilly (Indianapolis, IN), Extend Nutrition (St. Louis, MO), Insulet Corporation (Bedford, MA), LifeScan (Milpitas, CA), Medtronic Diabetes (Minneapolis, MN), Nipro Home Diagnostics (Ft. Lauderdale, FL), Nova Diabetes Care (Billerica, MA), Omron (Shelton, CT), Perrigo Diabetes Care (Allegan, MI), Roche Diabetes Care (Indianapolis, IN), and Sanofi (Bridgewater, NJ).

. Louis, MO), Insulet Corporation (Bedford, MA), LifeScan (Milpitas, CA), Medtronic Diabetes (Minneapolis, MN), Nipro Home Diagnostics (Ft. Lauderdale, FL), Nova Diabetes Care (Billerica, MA), Omron (Shelton, CT), Perrigo Diabetes Care (Allegan, MI), Roche Diabetes Care (Indianapolis, IN), and Sanofi (Bridgewater, NJ). Duality of Interest. No potential conflicts of interest relevant to this article were reported. Author Contributions. B.H.B. wrote the manuscript and conducted the statistical analyses. S.D.-J., M.L., W.S., I.B., O.K., S.G., and J.M.L. wrote sections of, reviewed, and edited the manuscript. B.H.B. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.